Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.
Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient's liver, and allows comparing livers from several patients in a common framework of reference.
We present a framework for multi-level statistical shape analysis, applied to the study of anatomical
variability of abdominal organs. Statistical models were built hierarchically, allowing the representation of
different levels of detail. Principal factor analysis was used for decomposition of deformation fields
obtained from non-rigid registration at different levels, and provided a compact model to study shape
variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a
clustering technique of the deformation vectors was proposed. The analysis of deformation fields showed a
strong correlation with anatomical landmarks and known mechanical deformations in the abdomen.
Clusters of modes of deformation from fine-to-coarse levels explain tissue properties, and inter-organ
relationships. Our method further presents the automated hierarchical partitioning of organs into
anatomically significant components that represent potentially important constraints for abdominal
diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.
Image-guided, computer-assisted neurosurgery has emerged to improve localization and targeting, to provide a better anatomic definition of the surgical field, and to decrease invasiveness. Usually, in image-guided surgery, a computer displays the surgical field in a CT/MR environment, using axial, coronal or sagittal views, or even a 3D representation of the patient. Such a system forces the surgeon to look away from the surgical scene to the computer screen. Moreover, this kind of information, being pre-operative imaging, can not be modified during the operation, so it remains valid for guidance in the first stage of the surgical procedure, and mainly for rigid structures like bones. In order to solve the two constraints mentioned before, we are developing an ultrasoundguided surgical microscope. Such a system takes the advantage that surgical microscopy and ultrasound systems are already used in neurosurgery, so it does not add more complexity to the surgical procedure. We have integrated an optical tracking device in the microscope and an augmented reality overlay system with which we avoid the need to look away from the scene, providing correctly aligned surgical images with sub-millimeter accuracy. In addition to the standard CT and 3D views, we are able to track an ultrasound probe, and using a previous calibration and registration of the imaging, the image obtained is correctly projected to the overlay system, so the surgeon can always localize the target and verify the effects of the intervention. Several tests of the system have been already performed to evaluate the accuracy, and clinical experiments are currently in progress in order to validate the clinical usefulness of the system.
This paper presents a feasibility and evaluation study for using 2D ultrasound in conjunction with our statistical deformable bone model in the scope of computer-assisted surgery (CAS). The final aim is to provide the surgeon with an enhanced 3D visualization for surgical navigation in orthopaedic surgery without the need for preoperative CT or MRI scans. We unified our earlier work to combine several automatic methods for statistical bone shape prediction from a sparse set of surface points, and ultrasound segmentation and calibration to provide the intended rapid and accurate visualization. We compared the use of a tracked digitizing pointer to ultrasound to acquire landmarks and bone surface points for the estimation of two cast proximal femurs, where two users performed the experiments 5-6 times per scenario. The concept of CT-based error introduced in the paper is used to give an approximate quantitative value to the best hoped-for prediction error, or lower-bound error, for a given anatomy. The conclusions of this work were that the pointer-based approach produced good results, and although the ultrasound-based approach performed considerably worse on average, there were several cases where the results were comparable to the pointer-based approach. It was determined that the primary factor for poor ultrasound performance was the inaccurate localization of the three initial landmarks, which are used for the statistical shape model.
The analysis of shape variability of anatomical structures is of key importance in a number of clinical disciplines, as abnormality in shape can be related to certain diseases. Statistical shape analysis techniques commonly employed in the medical imaging community, such as Active Shape Models or Active Appearance Models rely on Principal Component Analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose Principal Factor Analysis (PFA) as an alternative to PCA and argue that PFA is a better suited technique for medical imaging applications. PFA provides a decomposition into modes of variation that are more easily interpretable, while still being a linear, efficient technique that performs dimensionality reduction (as opposed to Independent Component Analysis, ICA). Both PCA and PFA are described. Examples are provided for 2D landmark data of corpora callosa outlines, as well as vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI. The results show that PFA is a more descriptive tool for shape analysis, at a small cost in size (as in theory more components may be necessary to explain a given percentage of total variance in the data). In conclusion, we argue that it is important to study the potential of factor analysis techniques other than PCA for the application of shape analysis, and defend PFA as a good alternative.
The use of three dimensional models in planning and navigating computer assisted surgeries is now well established. These models provide intuitive visualization to the surgeons contributing to significantly better surgical outcomes. Models obtained from specifically acquired CT scans have the disadvantage that they induce high radiation dose to the patient. In this paper we propose a novel and stable method to construct a patient-specific model that provides an appropriate intra-operative 3D visualization without the need for a pre or intra-operative imaging. Patient specific data
consists of digitized landmarks and surface points that are obtained
intra-operatively. The 3D model is reconstructed by fitting a statistical deformable model to the minimal sparse digitized data. The statistical model is constructed using Principal Component Analysis from training objects. Our morphing scheme efficiently and accurately computes a Mahalanobis distance weighted least square fit of the deformable model to the 3D data model by solving a linear equation system. Relaxing the Mahalanobis distance term as additional points are incorporated enables our method to handle small and large sets of digitized points efficiently. Our novel incorporation of M-estimator based weighting of the digitized points
enables us to effectively reject outliers and compute stable models. Normalization of the input model data and the digitized points
makes our method size invariant and hence applicable directly to any
anatomical shape. The method also allows incorporation of non-spatial data such as patient height and weight. The predominant applications are hip and knee surgeries.